# ==============================================================================
# Copyright 2014 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

# daal4py PCA example for distributed memory systems; SPMD mode
# run like this:
#    mpirun -n 4 python ./pca_spmd.py

from pathlib import Path

from numpy import allclose, loadtxt

import daal4py as d4p

if __name__ == "__main__":
    # Initialize SPMD mode
    d4p.daalinit()

    # Each process gets its own data
    data_path = Path(__file__).parent / "data" / "distributed"
    infile = data_path / f"pca_normalized_{d4p.my_procid() + 1}.csv"

    # configure a PCA object to use svd instead of default correlation
    algo = d4p.pca(method="svdDense", distributed=True)
    # let's provide a file directly, not a table/array
    result1 = algo.compute(str(infile))

    # We can also load the data ourselves and provide the numpy array
    data = loadtxt(infile, delimiter=",")
    result2 = algo.compute(data)

    # PCA result objects provide eigenvalues, eigenvectors, means and variances
    assert allclose(result1.eigenvalues, result2.eigenvalues)
    assert allclose(result1.eigenvectors, result2.eigenvectors)
    assert (
        result1.means is None
        and result2.means is None
        or allclose(result1.means, result2.means)
    )
    assert (
        result1.variances is None
        and result2.variances is None
        or allclose(result1.variances, result2.variances)
    )

    print("All looks good!")
    d4p.daalfini()
